Semantic routing
Semantic routing is how a system decides which AI model should handle each request. When a customer asks to reschedule an appointment, that does not need the same model that analyzes a complex contract clause. A lightweight model handles the scheduling request in milliseconds for almost nothing. The powerful model handles the contract analysis with the depth it requires. Semantic routing makes this decision automatically by understanding the intent and complexity of each request and sending it to the right model. Without routing, you either overspend by sending everything to your most capable model or underperform by sending everything to your cheapest one.
Go deeper
Think of it like triage in an emergency room. A nurse evaluates every patient who walks in — not to treat them, but to decide who needs a trauma surgeon and who needs a bandage. The triage step itself is fast and cheap, but it prevents the trauma surgeon from spending time on minor cuts and ensures the critical cases get immediate expert attention.
In practice, a semantic router is usually a small, fast classifier that sits in front of your model fleet. It reads the incoming request, categorizes the intent and complexity, and routes accordingly. Simple factual lookups go to a small model. Multi-step reasoning goes to a large model. Domain-specific questions might go to a fine-tuned specialist. The router itself costs almost nothing to run — a fraction of a cent per decision — but the savings cascade. If sixty percent of your traffic is routine and you route it to a model that costs one-thirtieth of your premium model, you have just cut your inference bill in half without touching quality on the hard cases.
The sophistication comes in the routing logic. Naive routing uses keywords — any request mentioning 'legal' goes to the expensive model. Smart routing understands that 'what are your office hours' mentioned in a legal context is still a simple lookup. The best routing systems learn from outcomes: when the cheap model gets it wrong, the router learns to send similar requests to the better model next time. This feedback loop means your routing gets more accurate and more cost-efficient over time.
Questions to ask
- What percentage of our AI interactions are routine enough for a smaller model?
- Do we have visibility into per-query costs across different model tiers?
- If we implemented routing tomorrow, which request categories would we route differently?